app.py.orig

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import folium
import netCDF4 as nc

# Load NetCDF data
path = './data/RN_KMA_NetCDF_2023081421.NC'
df = nc.Dataset(path)

df_var = df.variables['rain'][:]
rain_array = np.array(df_var)

# Load latitude and longitude data
lat_data = pd.read_csv('./data/dongne_lat_info.txt', header=None).values
lon_data = pd.read_csv('./data/dongne_lon_info.txt', header=None).values

latitude_array = lat_data
longitude_array = lon_data

# Trim latitude and longitude data
latitude_trimmed = latitude_array[:-1, :-1]
longitude_trimmed = longitude_array[:-1, :-1]

# Trim the rain_array to match the shape of trimmed latitude and longitude arrays
rain_trimmed = rain_array[:latitude_trimmed.shape[0], :latitude_trimmed.shape[1]]

# Create a Streamlit app
st.title('Rainfall Prediction - KMA')
st.write('Displaying Rainfall Heatmap')

# Display the heatmap
aspect_ratio = (longitude_trimmed.max() - longitude_trimmed.min()) / (latitude_trimmed.max() - latitude_trimmed.min())
fig = plt.figure(figsize=(6 * aspect_ratio, 6))
plt.imshow(rain_trimmed, cmap='rainbow', extent=[longitude_trimmed.min(), longitude_trimmed.max(), latitude_trimmed.min(), latitude_trimmed.max()], vmax=5)
plt.colorbar(label='mm/hr')
plt.title('Rainfall_Pred_KMA')
plt.xlabel('Longitude')
plt.ylabel('Latitude')

st.pyplot(fig)